Data Are Missing Again—Reconstruction of Power Generation Data Using k-Nearest Neighbors and Spectral Graph Theory

Amandine Pierrot, Pierre Pinson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing nearest neighbor imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.
Original languageEnglish
Article numbere2962
JournalWind Energy
Volume28
Issue number1
Number of pages13
ISSN1095-4244
DOIs
Publication statusPublished - 2025

Keywords

  • Laplacian eigenmaps
  • Missing data
  • Nadaraya-Watson estimators
  • Time series
  • Wind power forecasting

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